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AI Tools Usage

Analyze how developers use AI coding tools like GitHub Copilot and Cursor. This view surfaces active users, acceptance rates, and code contribution trends so you can evaluate trust, engagement, and real productivity impact across teams.

Steven Silverstone
Updated by Steven Silverstone

The AI Tools Usage panel measures how developers use supported AI coding tools across your organization. LinearB currently supports GitHub Copilot and Cursor.

Chatbot and bot accounts are excluded from all metrics.
Summary
  • Track AI tool adoption and usage across developers.
  • Measure trust and value using acceptance and trend metrics.
  • Use drilldowns (where available) to move from percentages to underlying counts.
What the panel measures

For each supported tool, the panel provides the following metrics:

  • Active users – Number of developers actively using the tool. Measured by counting developers who accept AI code suggestions, use AI chat, or trigger AI-powered PR summaries. Authentication-only events are excluded.
  • Acceptance rate – Percentage of AI suggestions accepted into code. Measured by comparing suggestions offered vs. suggestions accepted.
  • Code acceptance – A trend chart showing accepted AI-generated code over time. Measured by grouping acceptances by day.
AI Tools Usage panel showing adoption and code acceptance trends
GitHub Copilot

LinearB tracks GitHub Copilot using GitHub’s official Usage API.

  • Active Users – Users with any Copilot activity in a given day (for example, receiving a suggestion, accepting a suggestion, or prompting chat). Authentication-only events are excluded.
  • Engaged Users – Users who actively interacted with Copilot features (for example, accepting suggestions, prompting chat, or triggering a PR Summary). Authentication-only events are excluded.
  • Acceptance Rate – Percentage of Copilot suggestions accepted into code.
  • Code Acceptance – Trend of accepted Copilot-generated code over time, grouped by day.
GitHub Copilot usage metrics in LinearB
Cursor
  • Active Users – Developers using Cursor daily.
  • Code Acceptance – Number of AI-generated code lines accepted, grouped by day.
Cursor usage metrics in LinearB
Deeper visibility into coding behavior

The AI Tools Usage panel (currently limited to GitHub Copilot) provides deeper drilldowns so you can move from high-level percentages to the underlying counts and trends.

  • Active Users (%)
    • Percentage of developers actively using Copilot.
    • Click to see the number of users who are not using Copilot.
  • Acceptance Rate (%)
    • How often Copilot’s suggestions are accepted.
    • Click to see the breakdown of accepted vs. rejected suggestions.
  • Lines Written
    • Trend of accepted Copilot-generated lines over time.
    • Hover or click to see the number of lines written by date.

Example: If Active Users are high but Acceptance Rate is low, developers may be experimenting without trusting suggestions yet. If both trend upward, the tool is delivering real value.

Deeper Copilot drilldowns showing active users, acceptance rate, and lines written
Adoption funnel

The panel also supports an adoption funnel view:

  1. Are developers trying the tool?
  2. Are its suggestions being trusted?
  3. Is it driving meaningful code volume?

If a developer uses Copilot as a co-author, their work is included as AI-assisted. Chatbot activity is not included.

Copilot adoption funnel visualization in LinearB

How did we do?

AI Iteration Summary for Teams

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